Random variable
Definition
Let be a probability space and let be a set with a σ-algebra . A random variable with values in is a function such that the preimage is in for each . A real random variable is a random variable with values in , with the Borel σ-algebra on .
Remarks
The condition says that is a measurable function. It makes an event, so is defined. A real function on is a random variable if and only if for each . For two rolls of a die, and the sum is a random variable. Sums, products and continuous functions of real random variables are random variables.
Explanations
L1 human
A random variable is a number that you read off from the result of the experiment. For two rolls of a die, the sum of the two numbers is a random variable. It is for the result and for the result .
What this node needs for W2
- edge instance_of in: one example (0 of 1)
- edge not_instance_of in: one non-example that fails one clause (0 of 1)
The writing levels say how complete the text of a node is. They do not measure its truth.
What depends on this
135 nodes use this node, directly or through other nodes.
- Variance 007QED3E0Y
- Chernoff bound 014PEBGDYF
- Chapman–Kolmogorov equation 01APZ8GDQH
- Bias of an estimator 01BD2TFFP4
- Identically distributed random variables 01X8YSDTBW
- Law of the unconscious statistician 01ZWX875AT
- Density of the normal distribution 027JF1FBB0
- Exponential distribution 028Z8WC5Y9
- Covariance matrix 02CBHV4ZZQ
- Chebyshev's inequality 02HZ1BK3MW
- Characteristic function of a sum of independent random variables 02JGYG0T7M
- Stationary distribution 02KYGSCTCN
- Bernoulli process 02XPXR1FBK
- Markov chain 033TJBZDG0
- Statistic 036756NFD6
- Law of total variance 03BY5XTNA4
- Variance of the normal distribution 03EESZDB9A
- Cauchy–Schwarz inequality for expectations 03PGHKAJG3
- Correlation coefficient 04CTY6XYWN
- Variance under translation 04CYN5G6RY
- Almost sure convergence 04P7D63W8P
- Law of total expectation 04RRB6HM00
- Probability density function 04YG690N5V
- Expectation of a product of independent random variables 05AW1G0WE1
- Likelihood function 05FSTXM92W
- Markov's inequality 05PWWHYBX5
- Standard score 05RZ4YZG4E
- Poisson point process 05TG0SE4HT
- Gamma distribution 06395GBNWD
- De Moivre–Laplace theorem 065X9S8CM3
- Kolmogorov's zero–one law 073Q8Z94KG
- Independent random variables are uncorrelated 077GN9HDR6
- Convolution of probability distributions 07CHJ5YX28
- Normal distribution 07RXBVTVZB
- Mean of the Poisson distribution 086HEVXR9Z
- Statistical hypothesis test 087P03NEND
- Independence means the joint law is the product of the marginals 08QCA2F1NX
- Median 08W2CW8PKW
- Method of moments 09J1H07VAH
- Mean of the normal distribution 09PR1V6RWR
- Independent and identically distributed random variables 0AJN7WHBJZ
- Marginal distribution 0AXP2Z5N9H
- Moment-generating function of the normal distribution 0B1G4E21G7
- Quantile function 0B3Q3XDM5X
- Weak law of large numbers 0BFDHF2H7N
- Random walk 0C08YMV95H
- Random variable with a density 0C21RZG5V2
- Uncorrelated random variables that are not independent 0C6J8BQXVR
- Kurtosis 0CBQVVNBMP
- Martingale 0CDVK9RQBH
- Variance of a sum 0D86GJEZ39
- Mean of the geometric distribution 0DB0R4N5XX
- Expected value 0DCT0QZVQ1
- Strong law of large numbers 0DFWHQ839T
- Covariance 0DJ8W3HP2D
- Independence of two random variables 0E9502NT92
- Neyman–Pearson lemma 0EE19CFEEF
- Convergence in probability 0EFDDXTVD6
- P-value 0EHYZFZ4PK
- Chi-squared test 0EN0V3NY20
- Convergence in probability implies convergence in distribution 0EPTDZPJ83
- Central limit theorem 0EPZC4Z1FC
- 68–95–99.7 rule 0EYQ03XCR3
- Transition matrix 0F1KBVY161
- Conditional variance 0F1SN93KXE
- Lévy's continuity theorem 0FPWF7TTMB
- Convergence of random variables 0FQ968YKA1
- Maximum likelihood estimation 0FREPFKNS7
- Multivariate normal distribution 0G558GJQMW
- Skewness 0GQ3H7VW3V
- Galton–Watson process 0H631CZP5K
- Markov property 0HB8SXHPFA
- Student's t-test 0HYP606RYF
- Discrete random variable 0J6Z6H5VQJ
- Bernoulli distribution 0J97P5KZY0
- Convergence in distribution 0JDAQHWA9N
- The cumulative distribution function determines the distribution 0JEVZAFDY6
- Sample variance 0JN9P4A944
- Bayesian inference 0JWRJX2RMP
- Confidence interval 0K4NMMJBG2
- Variance under scaling 0KVQ9D86BM
- Discrete uniform distribution 0KYP951QWH
- Coupon collector's problem 0MXCP7MXWJ
- Cumulative distribution function 0MXMS3AE13
- Characteristic function 0MYDNRT7PP
- Cauchy distribution 0NA6Q82YR7
- Variance as the mean of the square minus the square of the mean 0NMF95YK8W
- Moment-generating function 0NNRNGKMFM
- Student's t-distribution 0NQ5798SB1
- Characteristic function of the normal distribution 0NS0TDA4E2
- Sum of independent normal random variables 0NXGPJWNFN
- Linearity of expectation 0P7PTC8DDE
- Moment 0PH0VHTQS0
- Beta distribution 0PP5ZAJPP7
- Expectation with respect to the binomial distribution 0PVCTCKD3C
- Cumulant generating function 0Q2QC81VTW
- Joint probability distribution 0QAR0XDZH4
- Stochastic process 0QH5ENKZM1
- Sample mean 0QNRBHWV35
- Type I and type II errors 0R41PVFA39
- Generating function of a sum of independent random variables 0REMT01N36
- Chi-squared distribution 0RFMMBHFTV
- Limit of the cumulative distribution function at infinity 0RJ5TZH6G0
- Hypergeometric distribution 0SPBPW3K61
- Moments are derivatives of the moment-generating function at zero 0STK8KEY9G
- Almost sure convergence implies convergence in probability 0SVN6X695K
- The characteristic function determines the distribution 0T8BYAFEXX
- Law of a random variable 0TST4MHA3A
- Convergence in mean 0TTA57SEJG
- Estimator 0TTNRP5PFQ
- Variance of a sum of independent random variables 0V0778DCW9
- Mean squared error 0V4VSCBXP3
- Poisson distribution 0VEFSSKX18
- Binomial distribution 0VRWZDWW6B
- Standard deviation 0VTN5RKNC5
- Probability mass function 0WF4EJP91W
- Gambler's ruin 0WFMKK7DFZ
- Continuous uniform distribution 0WGDPYR30Q
- Hoeffding's inequality 0WPDNXGSSN
- Moment-generating function of a sum of independent random variables 0WRZ170YEH
- Independence of random variables 0XCE070V8B
- Geometric distribution 0XCVV5Q500
- Monotonicity of the cumulative distribution function 0XD22PBMWA
- Consistent estimator 0XJVF81QF6
- Poisson limit theorem 0XKTZRWTXX
- Negative binomial distribution 0XN7KEVSSS
- Tail sum formula for expectation 0Y6QNF1J49
- Conditional expectation 0YAMJRK04E
- Absorbing Markov chain 0YB93GWTKK
- Sum of independent Poisson random variables 0YH1M56SBN
- Conditional probability distribution 0YVDC5PYDW
- Probability-generating function 0Z8PSSAVBA
- Memorylessness 0ZAZZ34WQS
- Central moment 0ZT24EC90K
- Law of large numbers 0ZVEAYVRC3
Sources
- Web https://en.wikipedia.org/wiki/Random_variable, Measure-theoretic definition
What links here
Used by
- Variance 007QED3E0Y
- Statistic 036756NFD6
- Almost sure convergence 04P7D63W8P
- Standard score 05RZ4YZG4E
- Median 08W2CW8PKW
- Random variable with a density 0C21RZG5V2
- Expected value 0DCT0QZVQ1
- Independence of two random variables 0E9502NT92
- Convergence in probability 0EFDDXTVD6
- Multivariate normal distribution 0G558GJQMW
- Discrete random variable 0J6Z6H5VQJ
- Convergence in distribution 0JDAQHWA9N
- Cumulative distribution function 0MXMS3AE13
- Joint probability distribution 0QAR0XDZH4
- Stochastic process 0QH5ENKZM1
- Law of a random variable 0TST4MHA3A
- Convergence in mean 0TTA57SEJG
- Probability mass function 0WF4EJP91W
- Independence of random variables 0XCE070V8B
- Conditional expectation 0YAMJRK04E